37 citations · 65 across the 7 of their papers we have counts for
7 papers
Satisficing Paths to Equilibrium, Generalized Weakly Acyclic Games, and Learning
Bora Yongacoglu, Gwendolen Hickey, Gürdal Arslan +2
Weakly acyclic games generalize potential games and have shown to be fundamental in the study of multi-agent learning as they allow for convergence to an equilibrium via best-respo…
Paths to Equilibrium in Games
Bora Yongacoglu, Gürdal Arslan, Lacra Pavel +1
In multi-agent reinforcement learning (MARL) and game theory, agents repeatedly interact and revise their strategies as new data arrives, producing a sequence of strategy profiles.…
Unsynchronized Decentralized Q-Learning: Two Timescale Analysis By Persistence
Bora Yongacoglu, Gürdal Arslan, Serdar Yüksel
Non-stationarity is a fundamental challenge in multi-agent reinforcement learning (MARL), where agents update their behaviour as they learn. Many theoretical advances in MARL avoid…
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games
Awni Altabaa, Bora Yongacoglu, Serdar Yüksel
Stochastic games are a popular framework for studying multi-agent reinforcement learning (MARL). Recent advances in MARL have focused primarily on games with finitely many states.…
Mean-Field Games With Finitely Many Players: Independent Learning and Subjectivity
Bora Yongacoglu, Gürdal Arslan, Serdar Yüksel
Independent learners are agents that employ single-agent algorithms in multi-agent systems, intentionally ignoring the effect of other strategic agents. This paper studies mean-fie…
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games
Bora Yongacoglu, Gürdal Arslan, Serdar Yüksel
In multi-agent reinforcement learning (MARL), independent learners are those that do not observe the actions of other agents in the system. Due to the decentralization of informati…
Decentralized Learning for Optimality in Stochastic Dynamic Teams and Games with Local Control and Global State Information
Bora Yongacoglu, Gürdal Arslan, Serdar Yüksel
Stochastic dynamic teams and games are rich models for decentralized systems and challenging testing grounds for multi-agent learning. Previous work that guaranteed team optimality…